Computer Vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Think about why this is traditionally difficult for a computer -- our brain naturally understands the photos and videos we see, but a computer simply displays it to us. In the field of computer vision, photographic or video input is fed into a deep learning model, and the machine learns to accurately identify and classify objects - and then react to what they "see."
Computer vision is used everywhere where a camera is present, such as facial recognition, autonomous vehicles, and object tracking.
You will cover both techniques and applications of computer vision which include:
Image Processing: We will cover the fundamental techniques of handling digital images. You'll learn how to load, display, and save images using Python libraries like OpenCV and PIL. Furthermore, you'll become familiar with basic image transformations like resizing, cropping, and rotating.
Feature Detection and Interpretation: Images are more than just a grid of pixels - they are composed of distinct features that help us interpret what we're seeing. You'll learn about different feature detection algorithms that can help a computer understand an image's content as well as how computers interpret features and images.
Convolutional Neural Networks(CNNs) & Deep Learning: CNNs are the backbone of most modern computer vision applications. They've been specialized to interpret image data and you'll learn about how they work and how to train them to recognize the contents of images. This will be a foray into the world of deep learning!
Our curriculum was designed for students with at least a pre-calculus background. You'll definitely learn more about math during these lessons, but it shouldn't be anything overly complicated. We've done a careful job balancing both depth of the material vs difficulty.